Abstract:Smart grids, as a key technology in the power industry, are becoming more and more prominent in cybersecurity issues. This study focuses on the evolution of adaptive security policies and their application in machine learning algorithms, aiming to address the cybersecurity challenges encountered in smart grids. The study first analyses the cybersecurity issues facing smart grids in detail, and establishes the centrality of machine learning in the construction of security policies. Following this, the theoretical foundations of adaptive security strategies are discussed in depth, including a clear definition of concepts, the construction of theoretical models, and an in-depth comparative analysis with traditional methods. On this basis, the key steps of algorithm optimisation are further analysed, covering feature engineering, careful selection and optimisation of machine learning models, and rigorous criteria and methods for model evaluation. The study also provides a prospective discussion on the technical implementation of adaptive security policies and an outlook on the future trend of smart grid security research. The innovation of this paper is that it integrates adaptive security policies with machine learning algorithms, which provides an innovative solution in the field of smart grid network security.